AIM To perform automatic gastric cancer risk classificationusing photofluorography for realizing effective mass screening as a preliminary study.METHODS We used data for 2100 subjects including X-ray images,pepsinogen...AIM To perform automatic gastric cancer risk classificationusing photofluorography for realizing effective mass screening as a preliminary study.METHODS We used data for 2100 subjects including X-ray images,pepsinogenⅠandⅡlevels,PGⅠ/PGⅡratio,Helicobacter pylori(H.pylori)antibody,H.pylori eradication history and interview sheets.We performed two-stage classification with our system.In the first stage,H.pylori infection status classification was performed,and H.pylori-infected subjects were automatically detected.In the second stage,we performed atrophic level classification to validate the effectiveness of our system.RESULTS Sensitivity,specificity and Youden index(YI)of H.pylori infection status classification were 0.884,0.895 and 0.779,respectively,in the first stage.In the second stage,sensitivity,specificity and YI of atrophic level classification for H.pylori-infected subjects were 0.777,0.824 and 0.601,respectively.CONCLUSION Although further improvements of the system are needed,experimental results indicated the effectiveness of machine learning techniques for estimation of gastric cancer risk.展开更多
文摘AIM To perform automatic gastric cancer risk classificationusing photofluorography for realizing effective mass screening as a preliminary study.METHODS We used data for 2100 subjects including X-ray images,pepsinogenⅠandⅡlevels,PGⅠ/PGⅡratio,Helicobacter pylori(H.pylori)antibody,H.pylori eradication history and interview sheets.We performed two-stage classification with our system.In the first stage,H.pylori infection status classification was performed,and H.pylori-infected subjects were automatically detected.In the second stage,we performed atrophic level classification to validate the effectiveness of our system.RESULTS Sensitivity,specificity and Youden index(YI)of H.pylori infection status classification were 0.884,0.895 and 0.779,respectively,in the first stage.In the second stage,sensitivity,specificity and YI of atrophic level classification for H.pylori-infected subjects were 0.777,0.824 and 0.601,respectively.CONCLUSION Although further improvements of the system are needed,experimental results indicated the effectiveness of machine learning techniques for estimation of gastric cancer risk.
